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AI ML Engineer

Insight Global

Live

Full-time Posted 24 days ago 3 min read

**AI/ML Engineer**

U.S. - Remote

**Must Haves**

* 5+ years of demonstrated experience building production-grade machine learning systems with measurable impacts; expertise in NLP and search and recommendation systems is preferred.
* Strong experience with Python
* Strong experience with Databricks technologies such as PySpark
* Hands-on experience with large language model (LLM) applications and AI agents, including retrieval-augmented generation, prompt optimization, fine-tuning, agent design, and evaluation methodologies; familiarity with prompt optimization frameworks like DSPy is preferred.
* Deep expertise in machine learning models and systems design, including classic models (e.g., XGBoost), modern deep learning and graph machine learning architectures (e.g., transformers-based models, graph neural networks (GNN)), and reinforcement learning systems.
* Proven ability to take models and agents from research to production, including optimization for latency and cost, implementation of monitoring and tracing, and development of reusable platforms or frameworks.
* Strong technical leadership and mentorship skills, with a track record of growing engineers, improving team velocity through automation, documentation, and tooling, and influencing architectural decisions without direct authority.
* Excellent communication and strategic thinking abilities, capable of aligning technical decisions with business outcomes, navigating ambiguity, and driving cross-functional collaboration.

**Job Description**

You’ll collaborate closely with cross-functional partners, including product management, software engineers, and business stakeholders, to identify opportunities, scope and deliver solutions, and measure their impact. This role blends technical leadership and hands-on execution: you’ll guide project direction, ensure quality, contribute code, and introduce best practices that scale team effectiveness. Your ability to influence strategy and translate complex ideas into business value will be key to advancing the organization’s data science and AI/ML agenda.

* Design and deploy end to end AI and machine learning solutions including machine learning and graph-based models, natural language processing (NLP) models, and large language model (LLM) based AI agents. Build robust pipelines for data ingestion, feature engineering, model training, validation, and real-time or batch inference.
* Develop and integrate large language model (LLM) applications using techniques such as fine-tuning, retrieval-augmented generation, and reinforcement learning. Build autonomous agents capable of multi-step reasoning and tool use in production environments.
* Lead the full model and agent development lifecycle, from problem definition and data exploration through experimentation, implementation, deployment, and monitoring. Ensure solutions are scalable, reliable, and aligned with business goals.
* Advocate and implement machine learning operations (MLOps) best practices including data monitoring and tracing, error analysis, automated retraining, model and prompt versioning, business metrics monitoring, and incident response.
* Collaborate across disciplines and provide technical leadership, working with product managers, engineers, and researchers to deliver impactful solutions. Mentor team members, lead design reviews, and promote best practices in AI and machine learning systems development.

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